Evidence map›Paper›PMID 37457825›Full record

ArticleAdvances in radiation oncology

Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer.

Joel A Pogue, Carlos E Cardenas, Joseph Harms, Michael H Soike, Adam J Kole, Craig S Schneider, Christopher Veale, Richard Popple, Jean-Guy Belliveau, Andrew M McDonald and 1 more

Open access · goldAbstract read
In one paragraph

Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
6.4field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 18 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Joel A PogueDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Carlos E CardenasDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Joseph HarmsDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Michael H SoikeDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Adam J KoleDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Craig S SchneiderDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Christopher VealeDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Richard PoppleDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Jean-Guy BelliveauDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Andrew M McDonaldDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
Dennis N StanleyDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama.
University of Alabama at Birmingham · US

Funding

NCI NIH HHS L30 CA284339
6 · The paper itself

Abstract

Purpose: Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer. Methods and Materials: Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board-approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review. Results: Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage ( Conclusions: A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.

Identifiers

PMID37457825
PMCPMC10344691
OpenAlexW4380882672

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.